Three-Dimensional Integral Imaging with Enhanced Lateral and Longitudinal Resolutions Using Multiple Pickup Positions

Self Cite

In this paper, we propose a lensless three-dimensional (3D) imaging under photon-starved conditions using diffraction grating and computational photon counting method. In conventional 3D imaging with and without the lens, 3D visualization of objects under photon-starved conditions may be difficult due to lack of photons. To solve this problem, our proposed method uses diffraction grating imaging as lensless 3D imaging and computational photon counting method for 3D visualization of objects under these conditions. In addition, to improve the visual quality of 3D images under severely photon-starved conditions, in this paper, multiple observation photon counting method with advanced statistical estimation such as Bayesian estimation is proposed. Multiple observation photon counting method can estimate the more accurate 3D images by remedying the random errors of photon occurrence because it can increase the samples of photons. To prove the ability of our proposed method, we implement the optical experiments and calculate the peak sidelobe ratio as the performance metric.

Section: Introductionmentioning

confidence: 99%

Lensless Three-Dimensional Imaging under Photon-Starved Conditions

Jang

Cho

2023

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“…These images are referred to as Elemental Images (EIs). 3D data can be digitally reconstructed from these multiple-perspective images using Computational Integral Imaging (CII) [8][9][10][11][12][13][14][15][16]. While Integral-Imaging can produce rich 3D data of the scene, it does not require the use of sources of illumination, as in time-of-flight cameras [17] or structured light imaging [18].…”

Section: Introductionmentioning

confidence: 99%

3D Object Detection via 2D Segmentation-Based Computational Integral Imaging Applied to a Real Video

Kadosh

Yitzhaky

2023

This study aims to achieve accurate three-dimensional (3D) localization of multiple objects in a complicated scene using passive imaging. It is challenging, as it requires accurate localization of the objects in all three dimensions given recorded 2D images. An integral imaging system captures the scene from multiple angles and is able to computationally produce blur-based depth information about the objects in the scene. We propose a method to detect and segment objects in a 3D space using integral-imaging data obtained by a video camera array. Using objects’ two-dimensional regions detected via deep learning, we employ local computational integral imaging in detected objects’ depth tubes to estimate the depth positions of the objects along the viewing axis. This method analyzes object-based blurring characteristics in the 3D environment efficiently. Our camera array produces an array of multiple-view videos of the scene, called elemental videos. Thus, the proposed 3D object detection applied to the video frames allows for 3D tracking of the objects with knowledge of their depth positions along the video. Results show successful 3D object detection with depth localization in a real-life scene based on passive integral imaging. Such outcomes have not been obtained in previous studies using integral imaging; mainly, the proposed method outperforms them in its ability to detect the depth locations of objects that are in close proximity to each other, regardless of the object size. This study may contribute when robust 3D object localization is desired with passive imaging, but it requires a camera or lens array imaging apparatus.

“…Many studies have been reported to overcome the drawbacks of the integral imaging [2][3][4][5][6][7][8][9][10][11][12][13]. To improve the viewing resolution of 3D images, the moving array lenslet technique (MALT) [6] was proposed.…”

Section: Introductionmentioning

confidence: 99%

“…Synthetic aperture integral imaging (SAII) [7], which uses a camera array, can improve the viewing resolution of 3D images since each elemental image has the same resolution as the image sensor. In [8], the SAII is further merged with an axially distributed sensing method to improve the lateral and longitudinal resolutions of 3D objects. However, it is not cost-effective and there exists a synchronization problem between cameras.…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Image Transmission of Integral Imaging through Wireless MIMO Channel

Lim

Cho

2023

Self Cite

For the reconstruction of high-resolution 3D digital content in integral imaging, an efficient wireless 3D image transmission system is required to convey a large number of elemental images without a communication bottleneck. To support a high transmission rate, we herein propose a novel wireless three-dimensional (3D) image transmission and reception strategy based on the multiple-input multiple-output (MIMO) technique. By exploiting the spatial multiplexing capability, multiple elemental images are transmitted simultaneously through the wireless MIMO channel, and recovered with a linear receiver such as matched filter, zero forcing, or minimum mean squared error combiners. Using the recovered elemental images, a 3D image can be reconstructed using volumetric computational reconstruction (VCR) with non-uniform shifting pixels. Although the received elemental images are corrupted by the wireless channel and inter-stream interference, the averaging effect of the VCR can improve the visual quality of the reconstructed 3D images. The numerical results validate that the proposed system can achieve excellent 3D reconstruction performance in terms of the visual quality and peak sidelobe ratio though a large number of elemental images are transmitted simultaneously over the wireless MIMO channel.